AI Field Engineer (Enterprise)

Join Fireworks as an AI Field Engineer, where you'll work closely with ambitious customers and technology partners to turn complex AI problems into production systems. You'll be hands-on in building POCs, MVPs, and production integrations, while also engaging in executive-level conversations about architecture, strategy, and business outcomes. This role requires strong technical delivery and executive presence, as you'll manage executive relationships, align teams, and stay hands-on in the code.

• Concevoir et mettre en œuvre des preuves de concept (POC) et des produits minimum viables (MVP) en collaboration avec les équipes d'ingénierie des clients.
• Gérer la relation technique avec le client depuis le premier engagement jusqu'au déploiement en production, en établissant la confiance avec les ingénieurs ML et les VP.
• Identifier les points de douleur récurrents des clients et les traduire en propositions de produits concrètes, en travaillant directement avec l'ingénierie et le produit pour expédier les corrections et les fonctionnalités.

• Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering
• 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder
• Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment
• Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon
• Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure
• Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)
• 10+ years in technical field or engineering roles
• Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads
• Experience operating as a technical authority inside a customer's environment building within their infrastructure, navigating their constraints, and shipping code that runs in their production systems
• Track record taking GenAI POCs from prototype to production-scale deployments
• Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains
• Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)